Image Reconstruction and Motion Compensation Methods for Fast MRI Chaoping

Magnetic resonance imaging (MRI) is one of the most widely used methods in medical imaging and can provide various soft tissue contrasts for various anatomies. MRI forms images from scanner acquired k-space signals using various imaging pulse sequences. Such acquisitions can take long times, which is one of the major drawbacks of MRI. To speed up, people have developed numerous fast imaging sequences, trying to acquire more k-space signals in shorter time. Another way to accelerate the imaging is subsampling the k-space, which imposes challenges for reconstruction. The clinical MRI scans are usually performed with a multi-channel receive coil and/or using multiple pulse sequence settings. The signal correlation among the channels or different sequence settings provides opportunities for reconstructing subsampled k-space. This thesis proposes multiple auto-calibrated reconstruction methods by exploiting such signal correlation. In this thesis, the autocalibration is not only used to accelerate the imaging but also to compensate for the motion that happens during the scanning. In addition to the conventional linear way of exploiting the signal correlation, we also explored non-linear autocalibration using a neural network. Finally, we explored the potential of acceleration with a deep learning inverse problem solver by exploiting further the shared knowledge between image reconstruction and relaxometry parameter mapping for quantitative MRI.In Chapter 2 we propose a reconstruction method, APIR4GRASE, for the GRASE sequence. The GRASE sequence interleaves the spin echoes and gradient echoes in the acquisition and therefore suffers from modulation artifacts from the mixed echo types. The APIR4GRASE integrates autocalibrated parallel imaging reconstruction among the different echo types with the selected optimized sampling patterns, achieving better image quality with less aliasing artifacts and noise amplification than the conventional 3D-GRASE. It reconstructs images using all echo types as virtual coil channels, in contrast to GRAPPA which individually reconstructs each echo type. The optimal sampling patterns of k-space associated with the proposed reconstruction method require exhaustive search, although for similar anatomies and scanning settings, a one time search with retrospective subsampling would be near optimal for prospective acquisitions. APIR4GRASE assumes spatially smooth T2* decay between the spin echo and the gradient echo, which is typically true in the brain. In a prospective acquisition, it achieved 0.8 mm 3D isotropic T2-weighted brain imaging with scan time of 5.5 minutes, whereas the alternative conventional GRASE SORT imaging with a subsampling factor of 2 took 9.4 min. In Chapter 3 we propose APIR4EMC which reconstructs multi-contrast images with autocalibrated parallel imaging reconstruction by adding contrasts as virtual coils. It is extended from APIR4GRASE (Chapter 2) and reconstructs multiple contrasts instead of different echo types in a sequence. In the multi-contrast imaging, different contrasts are acquired separately with different protocols, and the signal evolution along the echo train is therefore also different. We compensate for the difference with stabilization and Fermi filtering, which has been proven in the experiments to be able to improve the image quality. We jointly optimized the k-space sampling patterns of the multi-contrast acquisitions with exhaustive search, similar as in Chapter 1. With APIR4EMC, we improve the image quality over GRAPPA, and achieved 1 mm 3D isotropic in-vivo multi-contrast (T1, T1-Fatsat, T2, PD, FLAIR) brain imaging with scan time of 7.5 minutes.In Chapter 4 we propose a retrospective translational motion compensation method for parallel imaging 3D FSE acquisitions. Assuming no motion within each echo train of the FSE, we estimate the motion parameters of every echo train. The method relies on the optimization of data consistency in the fully sampled ACS region. To allow this, the ACS region is expected to contain echoes from every train, for which we propose a radial spokes view ordering for the 3D Cartesian k-space. The optimization is solved by alternating the estimation between the GRAPPA prediction kernel and motion parameters. Experiments with simulated motion and acquired motion in in-vivo acquisitions results show that the proposed method is able to substantially reduce the motion artifacts of the motion corrupted acquisitions.In Chapter 5 we propose a scan specific, auto-calibrated k-space completion method for parallel imaging, APIR-Net, to reconstruct the full k-space from an undersampled k-space by exploiting the redundancy among the multiple channels in the receive coil. The proposed APIR-Net is featured with a decreasing number of feature maps when encoding layer goes deeper, and a constant spatial size for all feature maps. Unlike the conventional parallel imaging methods that estimate the prediction kernel and perform interpolation in a linear way, APIR-Net is able to learn nonlinear relations among sampled and unsampled positions in k-space. The experiments show that APIR-Net was able to reduce noise amplification and increase the visual image quality compared to the state-of-the-art ESPIRiT and RAKI methods in both phantom and in-vivo experiments, making APIR-Net a promising alternative in low SNR acquisitions.In Chapter 6 we propose qRIM to accelerate the quantitative MR imaging. It embeds a unified forward model for joint reconstruction and R2*-mapping from sparse data in a Recurrent Inference Machine (RIM), an iterative inverse problem solving network. The integrated prior of the unified forward model facilitates the exploitation of the shared knowledge between the reconstruction and parameter estimation, including the redundancy among TEs. In the experiments, the proposed qRIM reduced the mapping error as well as imaging blurriness compared to the alternative sequential model of image reconstruction and parameter fitting, and the reduction of the reconstruction error increased with acceleration factor. With qRIM, we achieved a stable R2* mapping of the human subcortex up to 9-fold acceleration.Finally, in Chapter 7, we discussed the contributions and limitations of this thesis, and proposed a few future perspectives. In conclusion, this thesis presented several new autocalibration methods to improve image quality of reconstruction in acquisitions with multiple imaging sequence settings. We also presented a neural network that exploits non-linear autocalibration and which is able to reconstruct better image for low SNR acquisitions than the state-of-the-art ESPIRiT and RAKI methods. In addition to reconstruction, we also presented a novel retrospective translational motion compensation method by exploiting autocalibrated signals with a specifically designed view ordering for the parallel imaging 3D FSE acquisitions. Further exploiting the shared knowledge between the image reconstruction and parameter mapping, we presented the qRIM method that is able to improve the reconstruction quality of R2*. The image reconstruction and motion compensation methods proposed in this thesis may contribute to the implementation of faster MRI methods in clinical practice.<br/

CMOS radiation sensor design in 130nm CMOS technology : a thesis presented in partial fulfillment of the requirements for the degree of Master of Engineering in Electronics and Computer Engineering at School of Engineering and Advanced Technology, Massey University, Albany Campus

The following Figures were removed for copyright reasons: 2.5 (=Fröhlich et al., 2013 Fig 2), 2.6 (=Vasović & Ristić, 2012 Fig 1), 4.2 & 4.3 (=Garcia-Moreno et al., 2013 Figs 2 & 8).This research work deals with a CMOS radiation sensor design, which covers a new open source floating-gate MOSFET (FGMOSFET) device model for analog circuit design, Floating Gate Radiation Field Effect Transistor (FGRADFET) design, FGRADFET sensor output circuit design and their layout implementation using the 130nm IBM CMOS process. At first, a new FGMOSFET device model to facilitate circuit design is presented. In this model, the floating gate is charged by the Fowler-Nordheim tunneling effect. The equations representing the new device model were explored and verified on MATLAB. Verilog-A script was employed to transfer the equations and build the complete device model. The new FGMOSFET circuit model was plugged-in as a pop-up menu component in a standard 130 nm CMOS technology design library so that it can be instanced directly on a schematic editor palette for analog circuit simulation and design in a similar fashion as the standard MOSFET devices. Furthermore, the thesis describes the radiation sensor of FGRADFET that has an extra silicon area (125μm×200μm) as an antenna to sense the radiation from the environment. There are 16 PMOS transistors (1μm×2μm each) beneath the edge of the antenna to charge the floating gate. A radiation sensor readout circuit is also designed for this sensor. This circuit includes differentiator, pre-amplify buffer, chopper amplifier, low-pass filter and single-ended output amplifier. This integrated dosimeter has a 3.205mW power consumption and 2.33mGy -23mGy measuring range (The single-ended output voltage changes from 226mV to 967mV), which could be used for tremendous radiation exposure applications such as radiation therapy

Random Gabidulin Codes Achieve List Decoding Capacity in the Rank Metric

Gabidulin codes, serving as the rank-metric counterpart of Reed-Solomon codes, constitute an important class of maximum rank distance (MRD) codes. However, unlike the fruitful positive results about the list decoding of Reed-Solomon codes, results concerning the list decodability of Gabidulin codes in the rank metric are all negative so far. For example, in contrast to Reed-Solomon codes, which are always list decodable up to the Johnson bound in the Hamming metric, Raviv and Wachter-Zeh (IEEE TIT, 2016 and 2017) constructed a class of Gabidulin codes that are not even combinatorially list decodable beyond the unique decoding radius in the rank metric. Proving the existence of Gabidulin codes with good combinatorial list decodability in the rank metric has remained a long-standing open problem. In this paper, we resolve the aforementioned open problem by showing that, with high probability, random Gabidulin codes over sufficiently large alphabets attain the optimal generalized Singleton bound for list decoding in the rank metric. In particular, they achieve list decoding capacity in the rank metric. Our work is significantly influenced by the recent breakthroughs in the combinatorial list decodability of Reed-Solomon codes, especially the work by Brakensiek, Gopi, and Makam (STOC 2023). Our major technical contributions, which may hold independent interest, consist of the following: (1) We initiate the study of ``higher order MRD codes'' and provide a novel unified theory, which runs parallel to the theory of ``higher order MDS codes'' developed by BGM. (2) We prove a natural analog of the GM-MDS theorem, proven by Lovett (FOCS 2018) and Yildiz and Hassibi (IEEE TIT, 2019), which we call the GM-MRD theorem. In particular, our GM-MRD theorem for Gabidulin codes are strictly stronger than the GM-MDS theorem for Gabidulin codes, proven by Yildiz and Hassibi (IEEE TIT, 2019)

Experiment and verification of fine gridded precipitation forecast fusion correction in Sichuan

Fine-scale quantitative precipitation forecast is a key issue and challenge in weather forecasting services. In this study, based on hourly precipitation from the 1 km× 1 km resolution Southwest China WRF-based Intelligent Numeric Grid forecast System (SWC-WINGS), a fusion-corrected experiment was conducted using time lag and probability matching methods. The fusion-corrected forecast of hourly pre⁃ cipitation was then verified utilizing the CMA Multi-source Precipitation Analysis System (CMAPS) three-source merged precipitation obser⁃ vation grid data from 1 July to 31 August 2022 in Sichuan. Finally, the fusion-corrected method was applied to a short-term heavy precipita⁃ tion process over the western Sichuan Basin. The results show that: (1) Compared with the model precipitation forecasts, the time-lagged en⁃ semble forecast was over-optimistic for small-scale precipitation and over-conservative for large-scale precipitation. (2) However, the fu⁃ sion-corrected method by time lag and probability matching methods overcame the above difficulties and showed significant improvement in the TS score, particularly in 1~2 h nowcast time. The TS score for hourly precipitation exceeding 0.1 mm, 5 mm, 10 mm, and 20 mm were in⁃ creased on average by 7.2%, 17.2%, 28.3%, and 36.3%, respectively. (3) A case studies also showed that the fusion-corrected method had good improvement and correction capabilities on the hourly precipitation forecast, especially for large-scale precipitation forecasts

Frequent Mutations in Natural Killer/T Cell Lymphoma

Extranodal natural killer (NK)/T cell lymphoma (ENKTL-NT or NKTCL), with its aggressive nature and poor prognosis, has been widely studied to discover more effective treatment options. Various somatic gene alterations have been identified by traditional Sanger sequencing. However, recently, novel gene mutations in NKTCL have been revealed by next-generation sequencing (NGS) technology, suggesting the potential for novel targeted therapies. This review discusses recurrent aberrations in NKTCL detected by NGS, which can be categorized into three main groups, specifically, tumor suppressors (TP53, DDX3X, and MGA), the JAK/STAT cascade, and epigenetic modifiers (KMT2D, BCOR, ARID1A, and EP300). Some epigenetic dysregulation and DDX3X mutation, which have been rarely identified by traditional sequencing technology, were recently uncovered with high frequencies by NGS. In this review, we summarize the mutational frequencies of various genes in NKTCL. In general, based on our analysis, BCOR is the most frequently mutated gene (16.9%), followed by TP53 (14.7%), and DDX3X (13.6%). The characterization of such genes provides new insight into the pathogenesis of this disease and indicates new biomarkers or therapeutic targets

Revisiting Multi-modal 3D Semantic Segmentation in Real-world Autonomous Driving

LiDAR and camera are two critical sensors for multi-modal 3D semantic segmentation and are supposed to be fused efficiently and robustly to promise safety in various real-world scenarios. However, existing multi-modal methods face two key challenges: 1) difficulty with efficient deployment and real-time execution; and 2) drastic performance degradation under weak calibration between LiDAR and cameras. To address these challenges, we propose CPGNet-LCF, a new multi-modal fusion framework extending the LiDAR-only CPGNet. CPGNet-LCF solves the first challenge by inheriting the easy deployment and real-time capabilities of CPGNet. For the second challenge, we introduce a novel weak calibration knowledge distillation strategy during training to improve the robustness against the weak calibration. CPGNet-LCF achieves state-of-the-art performance on the nuScenes and SemanticKITTI benchmarks. Remarkably, it can be easily deployed to run in 20ms per frame on a single Tesla V100 GPU using TensorRT TF16 mode. Furthermore, we benchmark performance over four weak calibration levels, demonstrating the robustness of our proposed approach.Comment: 7 pages, 3 figure

Study on the May 28 Birch high-altitude and long-runout ice-rock avalanche in the Swiss Alps

On May 28, 2025, a high-altitude and long-runout ice-rock avalanche disaster occurred at the Birch Glacier in the Alps of the Valais region in southern Switzerland. This incident completely devastated the downstream towns of Blatten and Ried, leading to the emergency evacuation of over 300 individuals, with one person reported missing. This study presents a systematic investigation into the developmental characteristics, evolutionary processes, and disaster dynamics of the “5•28” Birch high-altitude and long-runout ice-rock avalanche, utilizing multi-temporal satellite remote sensing images, UAV data collected pre- and post-disaster, landquake signal, and on-site video footage. Preliminary results indicate that the Nesthorn Peak, located at a relative altitude of approximately 300 meters on the south side of the upper Birch Glacier, frequently experienced rockfalls driven by a combination of global climate warming and freeze-thaw cycles. While the accumulated debris on the glacier surface suppressed glacial ablation, it enhanced plastic flow, intensified bulging at the glacier front, and promoted the expansion of ice crevasses. Remote sensing interpretation revealed that the glacier area has expanded by approximately 44% over the past decade, with the glacier tongue advancing about 110 meters. During the disaster, around 3.0×106 m3 of wedge-shaped sliding mass experienced high-altitude instability, continually impacting the lower Birch Glacier at a velocity of about 36 m/s. This triggered a total instability involving approximately 6.0×106 m3 of glacial material and its covered debris, which subsequently transformed into a rapidly moving ice-rock avalanche that surged out of the valley at an average speed of 64 m/s, accumulating upon collision with the opposite mountainside. Such high-altitude and long-runout geological disasters, characterized by ice-rock compositions and developed in high-mountains area, are widely distributed throughout the Himalayan orogenic belt in China, posing serious threats to the geological safety of major engineering projects. This research may provide useful references for disaster prevention and mitigation strategies